Course - Machine Learning Basics with R

© 2014 Persontyle Ltd. All rights reserved.
[Hands-on Introduction of
Supervised Machine Learning Methods]
DURATION – 2 DAY
MACHINE LEARNING
BASICS WITH R
R logo is trademark of the R Foundation, from http://www.r-project.org

www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.
Data generated through our activities captures plethora of information about
our identity, likes and dislikes etc. This information has tremendous value in
every aspect of human life. Programming computers to unravel this hidden
information is what Machine Learning is all about. It is the art and science of
scientifically deriving insights, patterns and predictions from data.
Though it has been an area of active research for over 50 years, Machine
Learning is currently undergoing a renaissance driven by Moore's law and the
rise of big data. Large private and public investment in the area has given us
self-driving cars, practical speech recognition, effective web search, and a
vastly improved understanding of the human genome. Computer based
Machine Learning algorithms now outperform humans on tasks such as
handwritten digit recognition, traffic sign recognition, and even on some
complex reasoning tasks as demonstrated by IBM's Watson winning Jeopardy
<MACHINE LEARNING>
“The field of machine learning is concerned with the
question of how to construct computer programs that
automatically improve with experience.” - Tom Mitchell
Machine Learning models and programs automatically make decisions from data
in order to achieve some goal or requirement. Machine learning models matter to
the world. Because they are;
#EFFICIENT
Machine Learning models predict and detect partners faster than any other
manual program or method.
#EFFECTIVE
Machine Learning models can do better job than humans when analysing and
predicting large scale and streaming data sets (big data).
#SCALE
Machine Learning models can provide solutions to large data problems that
traditional systems can not solve.

 Machine perception
 Computer vision, including object
recognition
 Natural language processing
 Pattern recognition
 Search engines
 Medical diagnosis
 Bioinformatics
 Brain-machine interfaces
 Detecting credit card fraud
 Stock market analysis
 Classifying DNA sequences
 Sentiment analysis
 Affective computing
 Information retrieval
 Recommender systems
MACHINE LEARNING CAN APPEAR IN MANY GUISES
Examples in the real world include handwritten recognition, weather
prediction, fraud detection, search, facial recognition, and so forth are all
examples of machine learning in the wild. Applications for Machine
Learning include:
“Over the past two decades Machine Learning has become one of the
mainstays of information technology and with that, a rather central, albeit
usually hidden, part of our life. With the ever increasing amounts of data
becoming available there is good reason to believe that smart data analysis
will become even more pervasive as a necessary ingredient for
technological progress.”
DR. ALEXANDER J. SMOLA, PROFESSOR, CARNEGIE MELLON UNIVERSITY

This course is meant to be a fast-paced, hands-on introduction to Machine
Learning using R. The course will be focusing mainly on basics of Machine
Learning methods and practical implementation of these methods to solve
real-world problems. This course aims to develop basic understanding of
supervised learning methods, through the use of the R programming
platform. It describes the different types of learning and the two main
categories of their applications: Classification and Regression. With a focus
on the former, it takes a close look at typical Machine Learning techniques
and how they apply on datasets akin to those encountered in the real world.
Our goal is to give you the basic skills that you need to understand
supervised Machine Learning algorithms and models, and interpret their
output, which is important for solving a range of data science problems.
Without getting too much into the mathematics of Machine Learning, this
course dwells on the ideas and principles of it, along with the two main
methods that all Machine Learning practitioners use.
With theory and hands-on sessions intertwined, the course illustrates the
usefulness of Machine Learning and how you can learn it effectively without
losing sight of its elegance and value. Apart from the Classification
techniques, you will learn about how to validate any classifier's
performance, when to use what, and how the new innovations come about.
MACHINE LEARNING
BASICS WITH R
“The ability to take data - to be able to understand it, to process
it, to extract value from it, to visualize it, to communicate it -
that’s going to be a hugely important skill in the next decades.”
Hal Varian

WHAT WILL YOU LEARN?
In this course you will learn, among other things:
+ What Machine Learning entails and why it is important
+ The different types of Learning, especially Supervised
Learning
+ How Classification and Regression fit in Machine Learning
+ Understand types of classifiers
+ Applied details of following algorithms:
+ Decision Tree and basic overview of Random Forests
+ k Nearest Neighbour (kNN) and a few of its variants
+ Bayes Classifier
+ How to use these algorithms in a variety of benchmark
datasets
+ How to fine-tune these algorithms for better performance
+ Validation metrics for a classifier's performance (ROC
curve, Accuracy Rate, F1-metric)
+ The various libraries in R for these types of classifiers
+ Which algorithm to choose based on the data you have
+ Data transformation
MACHINE LEARNING
BASICS WITH R
PREREQUISITES
Knowledge of R programming language and familiarity with linear algebra. Basic
familiarity with statistics and probability theory is recommended.
* If you don't know R then first attend the Getting Started with R and Data
Analysis workshop.

SCHEDULE
MACHINE LEARNING
BASICS WITH R
Day 1
Time Topic/Activity
09:00-10:00 What is Machine Learning (ML) and why is it important? Examples of applications
10:00-11:00 Different types of learning and where they are used
11:00-11:15 Coffee Break
11:15-12:00 Overview of Classification and Regression
12:00-13:00 Using R for Machine Learning
13:00-13:30 Lunch Break
13:30-14:15 LAB: Practical examples (using R): Boston Housing Prices (Regression)
14:15-14:45 Classification Learning
15:00-16:00 Decision Tree Learning (ID3, C4.5, and C5.0)
16:00-16:45 LAB: Decision Tree example using R: Japanese Credit Screening (Classification)
17:00-17:30 Boosting method for decision trees
17:30-18:30 LAB: Using the C5.0 Decision Tree (Credit Card dataset)
Day 2
Time Topic/Activity
09:30-10:00 Instance-based learning - the intuitive approach to ML
10:00-11:00 kNN algorithm. kNN bias.
11:00-11:30 Overview of some kNN variants
11:45-12:15 Curse of Dimensionality
12:15-13:00 Guest Speaker
13:00-13:30 Lunch Break
13:30-14:00 Data transformation and normalization
14:00-14:45 LAB: Using kNN for Credit Card dataset
14:45-15:15 Bayes rule and Bayesian Learning. Dealing with noisy data
15:30-16:00 Bayesian Learning in action / Bayesian Classification
16:00-16:45 LAB: Using Bayes classifier for Credit Card dataset
16:45-17:00 Pros and Cons of methods covered
17:15-17:45 Validating results - ROC curve, Accuracy Rate, F1 metric, Confusion Table,
Precision, Recall
17:45-18:30 LAB: creating confusion table and basic validation metrics for all previous drills
(adult census data)

Persontyle trainers are passionate about meeting each participants learning
needs. They have been chosen both for their extensive practical Data
Science and Machine Learning experience and for their ability to educate
and interact with natural empathy. All of our trainers have worked on a
variety of data science and Machine Learning projects. They share their
academic knowledge and real-world experience and each individual adds
their own unique perspective to the course. Our trainers present in a style
that is informal, entertaining and highly interactive.
Guest Speakers
Business leaders, Machine Learning practitioners, and academic
researchers covering use cases, case studies and sharing practical
experience of applying Data Science and Machine Learning in their
organizations.
COURSE INSTRUCTORS
“A breakthrough in Machine Learning
would be worth ten Microsofts”
BILL GATES, CHAIRMAN, MICROSOFT
WHO SHOULD ATTEND
Anyone interested in learning and applying supervised machine learning
methods and R to solve real-world data problems. Ideal for people
interested in pursuing career in data science.
This hands-on workshop is aimed at business and technology professionals,
Developer, Architect, Manager, Data Analyst, BI Developer/Architect, QA,
Performance Engineers, Sales, Pre Sales and Marketing, Project Manager,
Public Services, Teaching Staff and all those who already have some basic
competence in statistics but wish to begin using R for machine learning for
the first time.
MACHINE LEARNING
BASICS WITH R

For corporate bookings or to organize on-site training email hello@persontyle.com or
call now +44 (0)20 3239 3141
Register Now
RETURN ON INVESTMENT (ROI)
CONVINCE YOUR BOSS
The advent of the data driven connected era means that analyzing massive
scale, messy, noisy, and unstructured data is going to increasingly form part
of everyone's work.
The School of Data Science learning programs provide a unique investment
opportunity that pay’s for itself many times over.
"For the best return on your money, pour your
purse into your head."
World-class
Instructors
Benjamin Franklin
Develop Practical
Data Science Skills
Real World
Industry Use Cases
Short Courses For
Time Convenience
Value For
Money
THE SCHOOL OF DATA SCIENCE
The School of Data Science, a project of Persontyle, specializes in designing and delivering
structured, relevant and practical learning experiences for all of us to understand data science in
simple human terms.
Follow us on Twitter @schooltds
Like us on Facebook
Get in touch! hello@personyyle.com
Limited seats. We encourage you to register as soon as you can.
www.persontyle.com/school
MACHINE LEARNING
BASICS WITH R

Course - Machine Learning Basics with R

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